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Aerospace Nuclear Science & Technology
Organized to promote the advancement of knowledge in the use of nuclear science and technologies in the aerospace application. Specialized nuclear-based technologies and applications are needed to advance the state-of-the-art in aerospace design, engineering and operations to explore planetary bodies in our solar system and beyond, plus enhance the safety of air travel, especially high speed air travel. Areas of interest will include but are not limited to the creation of nuclear-based power and propulsion systems, multifunctional materials to protect humans and electronic components from atmospheric, space, and nuclear power system radiation, human factor strategies for the safety and reliable operation of nuclear power and propulsion plants by non-specialized personnel and more.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
IAEA promoting nuclear energy with G20
The International Atomic Energy Agency launched a collaboration with the Group of 20 this week to highlight the key role that nuclear energy can play in achieving energy security and climate-change goals.
The aim of this first-of-its-kind partnership with G20—the world’s largest economic group—is to build momentum for nuclear power. This is the first time the IAEA has presented to G20 on issues relating to nuclear power.
Lei Jin, Kaushik Banerjee
Nuclear Science and Engineering | Volume 194 | Number 3 | March 2020 | Pages 190-206
Technical Paper | doi.org/10.1080/00295639.2019.1678104
Articles are hosted by Taylor and Francis Online.
Monte Carlo (MC) simulation is used to solve the eigenvalue form of the Boltzmann transport equation to estimate various parameters such as fuel pin flux distributions that are crucial for the safe and efficient operation of nuclear systems (e.g., a nuclear reactor). Monte Carlo eigenvalue simulation uses a sample mean over many stationary cycles (iterations) to estimate various parameters important to nuclear systems. A variance estimate of the sample mean is often used for calculating the confidence intervals. However, MC eigenvalue simulation variance estimators that ignore the intercycle correlation underestimate the true variance of the estimated quantity. This paper presents novel data-adaptive approaches based on a simple autoregressive (AR) model and sigmoid functions to improve MC variance estimation. The standard MC sample-based variance estimator (or naïve estimator) and the spectral density–based MC variance estimator are enhanced by adding data-adaptive components that reduce their bias and improve performance. By investigating the frequency pattern of the AR(1) (order 1) model, two adaptive spectral estimators and one adaptive naïve estimator are proposed. The proposed estimators manifest superior performance when applied to three test problems compared to the standard spectral density–based estimator previously introduced by the authors. These new estimators are straightforward, as they use online algorithms and do not require storage of tallies from all active cycles.